Explicit neural network-based nonlinear predictive control with low computational complexity

Authors:
Maciej Ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

This paper describes a nonlinear Model Predictive Control (MPC) algorithm based on neural models. Two neural models are used on-line: from a dynamic model the free trajectory (the influence of the past) is determined, the second neural network approximates the time-varying feedback law. In consequence, the algorithm is characterised by very low computational complexity because the control signal is calculated explicitly, without any on-line optimisation. Moreover, unlike other suboptimal MPC approaches, the necessity of model linearisation and matrix inversion is eliminated. The presented algorithm is compared with linearisation-based MPC and MPC with full nonlinear optimisation in terms of accuracy and computational complexity.